Thalamus segmentation using multi-modal feature classification: Validation and pilot study of an age-matched cohort

Abstract

Automatic segmentation of the thalamus can be used to measure differences and track changes in thalamic volume that may occur due to disease, injury or normal aging. An automatic thalamus segmentation algorithm incorporating features from diffusion tensor imaging (DTI) and thalamus priors constructed from multiple atlases is proposed. Multiple atlases with corresponding manual thalamus segmentations are registered to the target image and averaged to generate the thalamus prior. At each voxel in a region of interest around the thalamus, a multidimensional feature vector that includes the thalamus prior as well as a set of DTI features, including fractional anisotropy, mean diffusivity, and fiber orientation is formed. A random forest is trained to classify each voxel as belonging to the thalamus or background within the region of interest. Using a leave-one-out cross-validation on nine subjects, the proposed algorithm achieves a mean Dice score of 0.878 and 0.890 for the left and right thalami, respectively, which are higher Dice scores than the three state-of-art methods we compared to. We demonstrate the utility of the method with a pilot study exploring the difference in the thalamus fraction between 21 multiple sclerosis (MS) patients and 21 age-matched healthy controls. The left and right thalamic volumes (normalized by intracranial volumes) are larger in healthy controls by 7.6% and 7.3% respectively, compared to MS patients (though neither result is statistically significant).

Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in multiple sclerosis: A multicenter study

“…To our knowledge, this is the first multicenter study that compared automated thalamus segmentation methods and manual outlining, and evaluated their influence on the association of thalamus volume with cognition in MS patients in the presence of MS-related pathologies. Earlier research on this topic considered single-scanner data only ( Glaister et al, 2017 , Houtchens et al, 2007 , Popescu et al, 2016 ); or compared automated techniques without including manual outlining ( Derakhshan et al, 2010 , Popescu et al, 2016 ). When aiming to fully understand the relationship between thalamus atrophy and cognitive decline, automated methods may present a biased picture or reflect spurious correlations, since there have been reports that the algorithms may yield measurement errors that increase with increasing MS pathology such as WM lesions and atrophy ( Amiri et al, 2018 , Derakhshan et al, 2010 , Sastre-Garriga et al, 2020 ).…”

<p>Disrupted Brain Entropy And Functional Connectivity Patterns Of Thalamic Subregions In Major Depressive Disorder</p>

“…In recent years, there has been growing particular interest in examining thalamic dysfunction with the delineation of thalamic parcellation and various RSFC patterns 38,39,40. The thalamus is a communication conduit between subcortical brain areas and the cerebral cortex, such as in the thalamo-cortical system.…”

Quantitative vibratory sensation measurement is related to sensory cortical thickness in MS

“…The MPRAGE, T2‐w MRI and DTI were used to segment the thalamus using the RAFTS method, thalamic volumes are presented in Table . The RAFTS method has been shown to be superior to thalamic analyses with FSL First and FreeSurfer software packages . All processing and segmentation results were manually reviewed by a trained observer (BD).…”